Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations3489
Missing cells44
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory907.8 B

Variable types

Text2
DateTime1
Categorical17
Numeric6

Alerts

cant_apercibimientos has constant value "0.0" Constant
cant_MontoLimite has constant value "0.0" Constant
cluster_k5 has constant value "0" Constant
anio_preinscripcion is highly overall correlated with antiguedad and 1 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_sinMontoLimiteHigh correlation
cant_antecedentes is highly overall correlated with cant_suspensionesHigh correlation
cant_autenticado is highly overall correlated with cant_representante and 1 other fieldsHigh correlation
cant_noAutenticado is highly overall correlated with cant_sinMontoLimiteHigh correlation
cant_procesos_adjudicado is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_representante is highly overall correlated with cant_autenticado and 1 other fieldsHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_suspensiones is highly overall correlated with cant_antecedentesHigh correlation
monto_total_adjudicado is highly overall correlated with cant_procesos_adjudicadoHigh correlation
periodo_preinscripcion is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
Estado is highly imbalanced (60.9%) Imbalance
cant_suspensiones is highly imbalanced (96.0%) Imbalance
cant_antecedentes is highly imbalanced (95.8%) Imbalance
cant_representante is highly imbalanced (72.1%) Imbalance
cant_autenticado is highly imbalanced (51.4%) Imbalance
cant_noAutenticado is highly imbalanced (59.6%) Imbalance
CUIT has unique values Unique
Nombre has unique values Unique
monto_total_adjudicado has 55 (1.6%) zeros Zeros
antiguedad has 386 (11.1%) zeros Zeros
cant_socios has 110 (3.2%) zeros Zeros

Reproduction

Analysis started2025-07-08 14:18:38.207406
Analysis finished2025-07-08 14:18:42.464641
Duration4.26 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct3489
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size258.8 KiB
2025-07-08T11:18:42.596543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length11
Mean length10.967326
Min length9

Characters and Unicode

Total characters38265
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3489 ?
Unique (%)100.0%

Sample

1st row33712286089
2nd row30583184305
3rd row30644877805
4th row30710210000
5th row33561600959
ValueCountFrequency (%)
30714749206 1
 
< 0.1%
30717218430 1
 
< 0.1%
20061791741098 1
 
< 0.1%
30714903949 1
 
< 0.1%
30714760811 1
 
< 0.1%
30571863398 1
 
< 0.1%
30710827431 1
 
< 0.1%
30708197145 1
 
< 0.1%
30716665247 1
 
< 0.1%
33711217369 1
 
< 0.1%
Other values (3479) 3479
99.7%
2025-07-08T11:18:42.810867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6516
17.0%
3 5960
15.6%
7 4745
12.4%
1 4180
10.9%
6 3169
8.3%
5 3001
7.8%
9 2902
7.6%
4 2647
6.9%
8 2530
 
6.6%
2 2467
 
6.4%
Other values (26) 148
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6516
17.0%
3 5960
15.6%
7 4745
12.4%
1 4180
10.9%
6 3169
8.3%
5 3001
7.8%
9 2902
7.6%
4 2647
6.9%
8 2530
 
6.6%
2 2467
 
6.4%
Other values (26) 148
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6516
17.0%
3 5960
15.6%
7 4745
12.4%
1 4180
10.9%
6 3169
8.3%
5 3001
7.8%
9 2902
7.6%
4 2647
6.9%
8 2530
 
6.6%
2 2467
 
6.4%
Other values (26) 148
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6516
17.0%
3 5960
15.6%
7 4745
12.4%
1 4180
10.9%
6 3169
8.3%
5 3001
7.8%
9 2902
7.6%
4 2647
6.9%
8 2530
 
6.6%
2 2467
 
6.4%
Other values (26) 148
 
0.4%

Nombre
Text

Unique 

Distinct3489
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.3 KiB
2025-07-08T11:18:42.985589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length135
Median length82
Mean length21.625681
Min length3

Characters and Unicode

Total characters75452
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3489 ?
Unique (%)100.0%

Sample

1st rowCOMPAÑÍA DE HIGIENE
2nd rowMATAFUEGOS ORLANDO S.R.L.
3rd rowLA BLUSERI S.A.
4th rowTECNARAN SRL
5th rowRognoni y CIA SA
ValueCountFrequency (%)
s.a 876
 
7.5%
srl 865
 
7.4%
s.r.l 507
 
4.4%
de 444
 
3.8%
sa 407
 
3.5%
y 235
 
2.0%
argentina 120
 
1.0%
cooperativa 119
 
1.0%
trabajo 108
 
0.9%
la 90
 
0.8%
Other values (4592) 7872
67.6%
2025-07-08T11:18:43.265543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8154
 
10.8%
A 6071
 
8.0%
S 5534
 
7.3%
R 4307
 
5.7%
E 3774
 
5.0%
. 3652
 
4.8%
I 3637
 
4.8%
L 3261
 
4.3%
O 3200
 
4.2%
N 2525
 
3.3%
Other values (87) 31337
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8154
 
10.8%
A 6071
 
8.0%
S 5534
 
7.3%
R 4307
 
5.7%
E 3774
 
5.0%
. 3652
 
4.8%
I 3637
 
4.8%
L 3261
 
4.3%
O 3200
 
4.2%
N 2525
 
3.3%
Other values (87) 31337
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8154
 
10.8%
A 6071
 
8.0%
S 5534
 
7.3%
R 4307
 
5.7%
E 3774
 
5.0%
. 3652
 
4.8%
I 3637
 
4.8%
L 3261
 
4.3%
O 3200
 
4.2%
N 2525
 
3.3%
Other values (87) 31337
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8154
 
10.8%
A 6071
 
8.0%
S 5534
 
7.3%
R 4307
 
5.7%
E 3774
 
5.0%
. 3652
 
4.8%
I 3637
 
4.8%
L 3261
 
4.3%
O 3200
 
4.2%
N 2525
 
3.3%
Other values (87) 31337
41.5%
Distinct1225
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
Minimum2016-01-08 00:00:00
Maximum2022-12-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-08T11:18:43.343921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:43.446046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

Imbalance 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size265.3 KiB
Inscripto
2729 
Pre Inscripto
298 
Desactualizado Por Documentos Vencidos
289 
Desactualizado Por Mantencion Formulario
 
98
Desactualizado Por Clase
 
44
Other values (3)
 
31

Length

Max length40
Median length9
Mean length12.873316
Min length9

Characters and Unicode

Total characters44915
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowInscripto
2nd rowDesactualizado Por Documentos Vencidos
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 2729
78.2%
Pre Inscripto 298
 
8.5%
Desactualizado Por Documentos Vencidos 289
 
8.3%
Desactualizado Por Mantencion Formulario 98
 
2.8%
Desactualizado Por Clase 44
 
1.3%
En Evaluacion 15
 
0.4%
Con Solicitud De Baja 15
 
0.4%
Inhabilitado 1
 
< 0.1%

Length

2025-07-08T11:18:43.539873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:43.614661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 3027
59.4%
desactualizado 431
 
8.5%
por 431
 
8.5%
pre 298
 
5.8%
documentos 289
 
5.7%
vencidos 289
 
5.7%
mantencion 98
 
1.9%
formulario 98
 
1.9%
clase 44
 
0.9%
en 15
 
0.3%
Other values (6) 76
 
1.5%

Most occurring characters

ValueCountFrequency (%)
o 5096
11.3%
c 4164
9.3%
s 4080
9.1%
i 3990
8.9%
r 3952
8.8%
n 3945
8.8%
t 3861
8.6%
I 3028
 
6.7%
p 3027
 
6.7%
1607
 
3.6%
Other values (20) 8165
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5096
11.3%
c 4164
9.3%
s 4080
9.1%
i 3990
8.9%
r 3952
8.8%
n 3945
8.8%
t 3861
8.6%
I 3028
 
6.7%
p 3027
 
6.7%
1607
 
3.6%
Other values (20) 8165
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5096
11.3%
c 4164
9.3%
s 4080
9.1%
i 3990
8.9%
r 3952
8.8%
n 3945
8.8%
t 3861
8.6%
I 3028
 
6.7%
p 3027
 
6.7%
1607
 
3.6%
Other values (20) 8165
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5096
11.3%
c 4164
9.3%
s 4080
9.1%
i 3990
8.9%
r 3952
8.8%
n 3945
8.8%
t 3861
8.6%
I 3028
 
6.7%
p 3027
 
6.7%
1607
 
3.6%
Other values (20) 8165
18.2%

TipoSocietario
Categorical

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size373.4 KiB
Sociedad Responsabilidad Limitada
1425 
Sociedad Anónima
1395 
Otras Formas Societarias
193 
Persona Jurídica Extranjero Sin Sucursal
164 
Cooperativas
 
131
Other values (5)
181 

Length

Max length40
Median length33
Mean length24.48094
Min length12

Characters and Unicode

Total characters85414
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSociedad Responsabilidad Limitada
2nd rowSociedad Responsabilidad Limitada
3rd rowSociedad Anónima
4th rowSociedad Responsabilidad Limitada
5th rowSociedad Anónima

Common Values

ValueCountFrequency (%)
Sociedad Responsabilidad Limitada 1425
40.8%
Sociedad Anónima 1395
40.0%
Otras Formas Societarias 193
 
5.5%
Persona Jurídica Extranjero Sin Sucursal 164
 
4.7%
Cooperativas 131
 
3.8%
Organismo Publico 104
 
3.0%
Sociedades De Hecho 61
 
1.7%
Unión Transitoria de Empresas 9
 
0.3%
Persona Física 6
 
0.2%
Talleres protegidos de Producción 1
 
< 0.1%

Length

2025-07-08T11:18:43.710252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:43.796889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sociedad 2820
31.2%
responsabilidad 1425
15.8%
limitada 1425
15.8%
anónima 1395
15.4%
otras 193
 
2.1%
formas 193
 
2.1%
societarias 193
 
2.1%
persona 170
 
1.9%
jurídica 164
 
1.8%
extranjero 164
 
1.8%
Other values (15) 896
 
9.9%

Most occurring characters

ValueCountFrequency (%)
a 11810
13.8%
i 11064
13.0%
d 10213
12.0%
o 5569
 
6.5%
5549
 
6.5%
e 5169
 
6.1%
n 4845
 
5.7%
s 4094
 
4.8%
c 3575
 
4.2%
S 3402
 
4.0%
Other values (27) 20124
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11810
13.8%
i 11064
13.0%
d 10213
12.0%
o 5569
 
6.5%
5549
 
6.5%
e 5169
 
6.1%
n 4845
 
5.7%
s 4094
 
4.8%
c 3575
 
4.2%
S 3402
 
4.0%
Other values (27) 20124
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11810
13.8%
i 11064
13.0%
d 10213
12.0%
o 5569
 
6.5%
5549
 
6.5%
e 5169
 
6.1%
n 4845
 
5.7%
s 4094
 
4.8%
c 3575
 
4.2%
S 3402
 
4.0%
Other values (27) 20124
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11810
13.8%
i 11064
13.0%
d 10213
12.0%
o 5569
 
6.5%
5549
 
6.5%
e 5169
 
6.1%
n 4845
 
5.7%
s 4094
 
4.8%
c 3575
 
4.2%
S 3402
 
4.0%
Other values (27) 20124
23.6%

periodo_preinscripcion
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201796.54
Minimum201607
Maximum202211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2025-07-08T11:18:43.907238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201607
5-th percentile201610
Q1201703
median201710
Q3201904
95-th percentile202110
Maximum202211
Range604
Interquartile range (IQR)201

Descriptive statistics

Standard deviation164.01108
Coefficient of variation (CV)0.00081275468
Kurtosis-0.12371866
Mean201796.54
Median Absolute Deviation (MAD)98
Skewness0.94449588
Sum7.0406812 × 108
Variance26899.634
MonotonicityNot monotonic
2025-07-08T11:18:43.996741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201611 257
 
7.4%
201701 162
 
4.6%
201703 139
 
4.0%
201612 136
 
3.9%
201706 136
 
3.9%
201705 132
 
3.8%
201704 130
 
3.7%
201708 120
 
3.4%
201702 116
 
3.3%
201707 107
 
3.1%
Other values (67) 2054
58.9%
ValueCountFrequency (%)
201607 6
 
0.2%
201608 38
 
1.1%
201609 53
 
1.5%
201610 95
 
2.7%
201611 257
7.4%
201612 136
3.9%
201701 162
4.6%
201702 116
3.3%
201703 139
4.0%
201704 130
3.7%
ValueCountFrequency (%)
202211 7
0.2%
202210 5
 
0.1%
202209 10
0.3%
202208 11
0.3%
202207 11
0.3%
202206 13
0.4%
202205 15
0.4%
202204 16
0.5%
202203 14
0.4%
202202 14
0.4%

anio_preinscripcion
Categorical

High correlation 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size235.1 KiB
2017
1361 
2016
585 
2018
549 
2019
320 
2020
288 
Other values (2)
386 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters13956
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2017 1361
39.0%
2016 585
16.8%
2018 549
15.7%
2019 320
 
9.2%
2020 288
 
8.3%
2021 264
 
7.6%
2022 122
 
3.5%

Length

2025-07-08T11:18:44.092393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:44.154540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2017 1361
39.0%
2016 585
16.8%
2018 549
15.7%
2019 320
 
9.2%
2020 288
 
8.3%
2021 264
 
7.6%
2022 122
 
3.5%

Most occurring characters

ValueCountFrequency (%)
2 4285
30.7%
0 3777
27.1%
1 3079
22.1%
7 1361
 
9.8%
6 585
 
4.2%
8 549
 
3.9%
9 320
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13956
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4285
30.7%
0 3777
27.1%
1 3079
22.1%
7 1361
 
9.8%
6 585
 
4.2%
8 549
 
3.9%
9 320
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13956
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4285
30.7%
0 3777
27.1%
1 3079
22.1%
7 1361
 
9.8%
6 585
 
4.2%
8 549
 
3.9%
9 320
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13956
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4285
30.7%
0 3777
27.1%
1 3079
22.1%
7 1361
 
9.8%
6 585
 
4.2%
8 549
 
3.9%
9 320
 
2.3%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct123
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0980224
Minimum0
Maximum613
Zeros22
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2025-07-08T11:18:44.278834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q36
95-th percentile34
Maximum613
Range613
Interquartile range (IQR)5

Descriptive statistics

Standard deviation29.097119
Coefficient of variation (CV)3.1981807
Kurtosis148.25858
Mean9.0980224
Median Absolute Deviation (MAD)1
Skewness10.627688
Sum31743
Variance846.64234
MonotonicityNot monotonic
2025-07-08T11:18:44.379848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1219
34.9%
2 570
16.3%
3 335
 
9.6%
4 219
 
6.3%
5 138
 
4.0%
6 134
 
3.8%
7 82
 
2.4%
9 71
 
2.0%
8 63
 
1.8%
11 58
 
1.7%
Other values (113) 600
17.2%
ValueCountFrequency (%)
0 22
 
0.6%
1 1219
34.9%
2 570
16.3%
3 335
 
9.6%
4 219
 
6.3%
5 138
 
4.0%
6 134
 
3.8%
7 82
 
2.4%
8 63
 
1.8%
9 71
 
2.0%
ValueCountFrequency (%)
613 1
< 0.1%
468 1
< 0.1%
455 1
< 0.1%
417 1
< 0.1%
389 1
< 0.1%
384 1
< 0.1%
362 1
< 0.1%
361 1
< 0.1%
357 1
< 0.1%
332 1
< 0.1%

monto_total_adjudicado
Real number (ℝ)

High correlation  Zeros 

Distinct3421
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69821793
Minimum0
Maximum5.3233427 × 109
Zeros55
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2025-07-08T11:18:44.468773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25339.787
Q1818069.32
median5410883.4
Q329025161
95-th percentile2.7579401 × 108
Maximum5.3233427 × 109
Range5.3233427 × 109
Interquartile range (IQR)28207091

Descriptive statistics

Standard deviation3.0028187 × 108
Coefficient of variation (CV)4.3006897
Kurtosis124.9254
Mean69821793
Median Absolute Deviation (MAD)5285410.1
Skewness9.8778887
Sum2.4360823 × 1011
Variance9.01692 × 1016
MonotonicityNot monotonic
2025-07-08T11:18:44.725496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
1.6%
40000 3
 
0.1%
96000 2
 
0.1%
58802999.64 2
 
0.1%
612000 2
 
0.1%
1416666.667 2
 
0.1%
534820 2
 
0.1%
637500 2
 
0.1%
306000 2
 
0.1%
30600000 2
 
0.1%
Other values (3411) 3415
97.9%
ValueCountFrequency (%)
0 55
1.6%
0.023181818 1
 
< 0.1%
4.25 1
 
< 0.1%
9.471428571 1
 
< 0.1%
30.6 1
 
< 0.1%
59.49779221 1
 
< 0.1%
70.08857143 1
 
< 0.1%
102 1
 
< 0.1%
143.65 1
 
< 0.1%
516.24 1
 
< 0.1%
ValueCountFrequency (%)
5323342711 1
< 0.1%
5137870354 1
< 0.1%
5092092510 1
< 0.1%
4463290979 1
< 0.1%
3976509796 1
< 0.1%
3734355477 1
< 0.1%
3370404212 1
< 0.1%
3205078998 1
< 0.1%
3114732192 1
< 0.1%
2731314502 1
< 0.1%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1367154
Minimum0
Maximum5
Zeros386
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2025-07-08T11:18:44.799045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5685581
Coefficient of variation (CV)0.5000639
Kurtosis-0.54923999
Mean3.1367154
Median Absolute Deviation (MAD)1
Skewness-0.78298193
Sum10944
Variance2.4603746
MonotonicityNot monotonic
2025-07-08T11:18:44.862770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1361
39.0%
5 585
16.8%
3 549
15.7%
0 386
 
11.1%
2 320
 
9.2%
1 288
 
8.3%
ValueCountFrequency (%)
0 386
 
11.1%
1 288
 
8.3%
2 320
 
9.2%
3 549
15.7%
4 1361
39.0%
5 585
16.8%
ValueCountFrequency (%)
5 585
16.8%
4 1361
39.0%
3 549
15.7%
2 320
 
9.2%
1 288
 
8.3%
0 386
 
11.1%

provincia
Categorical

Distinct27
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size392.1 KiB
Ciudad Autónoma de Buenos Aires
1685 
Buenos Aires
706 
Córdoba
209 
Extranjera
 
164
Santa Fe
 
153
Other values (22)
572 

Length

Max length31
Median length19
Mean length20.003726
Min length5

Characters and Unicode

Total characters69793
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBuenos Aires
2nd rowCiudad Autónoma de Buenos Aires
3rd rowCiudad Autónoma de Buenos Aires
4th rowCiudad Autónoma de Buenos Aires
5th rowBuenos Aires

Common Values

ValueCountFrequency (%)
Ciudad Autónoma de Buenos Aires 1685
48.3%
Buenos Aires 706
20.2%
Córdoba 209
 
6.0%
Extranjera 164
 
4.7%
Santa Fe 153
 
4.4%
Mendoza 82
 
2.4%
Chubut 52
 
1.5%
Entre Rios 36
 
1.0%
Misiones 32
 
0.9%
Salta 31
 
0.9%
Other values (17) 339
 
9.7%

Length

2025-07-08T11:18:44.941168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aires 2391
21.1%
buenos 2391
21.1%
autónoma 1685
14.8%
ciudad 1685
14.8%
de 1685
14.8%
córdoba 209
 
1.8%
santa 173
 
1.5%
extranjera 164
 
1.4%
fe 153
 
1.3%
mendoza 82
 
0.7%
Other values (28) 733
 
6.5%

Most occurring characters

ValueCountFrequency (%)
7862
11.3%
e 7176
10.3%
u 6113
 
8.8%
d 5400
 
7.7%
s 4971
 
7.1%
n 4745
 
6.8%
a 4719
 
6.8%
o 4666
 
6.7%
i 4316
 
6.2%
A 4076
 
5.8%
Other values (30) 15749
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7862
11.3%
e 7176
10.3%
u 6113
 
8.8%
d 5400
 
7.7%
s 4971
 
7.1%
n 4745
 
6.8%
a 4719
 
6.8%
o 4666
 
6.7%
i 4316
 
6.2%
A 4076
 
5.8%
Other values (30) 15749
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7862
11.3%
e 7176
10.3%
u 6113
 
8.8%
d 5400
 
7.7%
s 4971
 
7.1%
n 4745
 
6.8%
a 4719
 
6.8%
o 4666
 
6.7%
i 4316
 
6.2%
A 4076
 
5.8%
Other values (30) 15749
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7862
11.3%
e 7176
10.3%
u 6113
 
8.8%
d 5400
 
7.7%
s 4971
 
7.1%
n 4745
 
6.8%
a 4719
 
6.8%
o 4666
 
6.7%
i 4316
 
6.2%
A 4076
 
5.8%
Other values (30) 15749
22.6%

cant_socios
Real number (ℝ)

Zeros 

Distinct18
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0217827
Minimum0
Maximum17
Zeros110
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2025-07-08T11:18:45.006945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile5
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.501943
Coefficient of variation (CV)0.7428805
Kurtosis22.014415
Mean2.0217827
Median Absolute Deviation (MAD)1
Skewness3.4975595
Sum7054
Variance2.2558327
MonotonicityNot monotonic
2025-07-08T11:18:45.080411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 1368
39.2%
1 1267
36.3%
3 407
 
11.7%
4 148
 
4.2%
0 110
 
3.2%
5 93
 
2.7%
6 42
 
1.2%
7 18
 
0.5%
8 10
 
0.3%
10 5
 
0.1%
Other values (8) 21
 
0.6%
ValueCountFrequency (%)
0 110
 
3.2%
1 1267
36.3%
2 1368
39.2%
3 407
 
11.7%
4 148
 
4.2%
5 93
 
2.7%
6 42
 
1.2%
7 18
 
0.5%
8 10
 
0.3%
9 5
 
0.1%
ValueCountFrequency (%)
17 3
 
0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 4
 
0.1%
13 1
 
< 0.1%
12 3
 
0.1%
11 3
 
0.1%
10 5
0.1%
9 5
0.1%
8 10
0.3%

cant_apercibimientos
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size231.7 KiB
0.0
3489 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10467
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3489
100.0%

Length

2025-07-08T11:18:45.159696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:45.201752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3489
100.0%

Most occurring characters

ValueCountFrequency (%)
0 6978
66.7%
. 3489
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6978
66.7%
. 3489
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6978
66.7%
. 3489
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6978
66.7%
. 3489
33.3%

cant_suspensiones
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size231.7 KiB
0.0
3474 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10467
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3474
99.6%
1.0 15
 
0.4%

Length

2025-07-08T11:18:45.255220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:45.300641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3474
99.6%
1.0 15
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 6963
66.5%
. 3489
33.3%
1 15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6963
66.5%
. 3489
33.3%
1 15
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6963
66.5%
. 3489
33.3%
1 15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6963
66.5%
. 3489
33.3%
1 15
 
0.1%

cant_antecedentes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size231.7 KiB
0.0
3473 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10467
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3473
99.5%
1.0 16
 
0.5%

Length

2025-07-08T11:18:45.356014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:45.394545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3473
99.5%
1.0 16
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 6962
66.5%
. 3489
33.3%
1 16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6962
66.5%
. 3489
33.3%
1 16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6962
66.5%
. 3489
33.3%
1 16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6962
66.5%
. 3489
33.3%
1 16
 
0.2%

cant_Apoderado
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size231.7 KiB
0.0
2404 
1.0
1075 
2.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10467
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2404
68.9%
1.0 1075
30.8%
2.0 10
 
0.3%

Length

2025-07-08T11:18:45.445706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:45.504849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2404
68.9%
1.0 1075
30.8%
2.0 10
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 5893
56.3%
. 3489
33.3%
1 1075
 
10.3%
2 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5893
56.3%
. 3489
33.3%
1 1075
 
10.3%
2 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5893
56.3%
. 3489
33.3%
1 1075
 
10.3%
2 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5893
56.3%
. 3489
33.3%
1 1075
 
10.3%
2 10
 
0.1%

cant_representante
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size231.7 KiB
1.0
3164 
2.0
 
218
0.0
 
79
3.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10467
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 3164
90.7%
2.0 218
 
6.2%
0.0 79
 
2.3%
3.0 28
 
0.8%

Length

2025-07-08T11:18:45.562348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:45.609291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3164
90.7%
2.0 218
 
6.2%
0.0 79
 
2.3%
3.0 28
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 3568
34.1%
. 3489
33.3%
1 3164
30.2%
2 218
 
2.1%
3 28
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3568
34.1%
. 3489
33.3%
1 3164
30.2%
2 218
 
2.1%
3 28
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3568
34.1%
. 3489
33.3%
1 3164
30.2%
2 218
 
2.1%
3 28
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3568
34.1%
. 3489
33.3%
1 3164
30.2%
2 218
 
2.1%
3 28
 
0.3%

cant_autenticado
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size231.7 KiB
1.0
2763 
2.0
710 
3.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10467
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 2763
79.2%
2.0 710
 
20.3%
3.0 16
 
0.5%

Length

2025-07-08T11:18:45.671791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:45.725052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2763
79.2%
2.0 710
 
20.3%
3.0 16
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 3489
33.3%
0 3489
33.3%
1 2763
26.4%
2 710
 
6.8%
3 16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3489
33.3%
0 3489
33.3%
1 2763
26.4%
2 710
 
6.8%
3 16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3489
33.3%
0 3489
33.3%
1 2763
26.4%
2 710
 
6.8%
3 16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3489
33.3%
0 3489
33.3%
1 2763
26.4%
2 710
 
6.8%
3 16
 
0.2%

cant_noAutenticado
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size231.7 KiB
0.0
2950 
1.0
530 
2.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10467
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2950
84.6%
1.0 530
 
15.2%
2.0 9
 
0.3%

Length

2025-07-08T11:18:45.788003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:45.826931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2950
84.6%
1.0 530
 
15.2%
2.0 9
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 6439
61.5%
. 3489
33.3%
1 530
 
5.1%
2 9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6439
61.5%
. 3489
33.3%
1 530
 
5.1%
2 9
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6439
61.5%
. 3489
33.3%
1 530
 
5.1%
2 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6439
61.5%
. 3489
33.3%
1 530
 
5.1%
2 9
 
0.1%

cant_sinMontoLimite
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size231.7 KiB
1.0
2227 
2.0
1234 
3.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10467
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 2227
63.8%
2.0 1234
35.4%
3.0 28
 
0.8%

Length

2025-07-08T11:18:45.890142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:45.937001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2227
63.8%
2.0 1234
35.4%
3.0 28
 
0.8%

Most occurring characters

ValueCountFrequency (%)
. 3489
33.3%
0 3489
33.3%
1 2227
21.3%
2 1234
 
11.8%
3 28
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3489
33.3%
0 3489
33.3%
1 2227
21.3%
2 1234
 
11.8%
3 28
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3489
33.3%
0 3489
33.3%
1 2227
21.3%
2 1234
 
11.8%
3 28
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3489
33.3%
0 3489
33.3%
1 2227
21.3%
2 1234
 
11.8%
3 28
 
0.3%

cant_MontoLimite
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size231.7 KiB
0.0
3489 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10467
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3489
100.0%

Length

2025-07-08T11:18:46.008324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:46.045539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3489
100.0%

Most occurring characters

ValueCountFrequency (%)
0 6978
66.7%
. 3489
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6978
66.7%
. 3489
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6978
66.7%
. 3489
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6978
66.7%
. 3489
33.3%

total_articulos_provee
Real number (ℝ)

Distinct380
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.800229
Minimum1
Maximum2339
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2025-07-08T11:18:46.108824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median10
Q340
95-th percentile287.6
Maximum2339
Range2338
Interquartile range (IQR)38

Descriptive statistics

Standard deviation152.94923
Coefficient of variation (CV)2.6011672
Kurtosis51.767312
Mean58.800229
Median Absolute Deviation (MAD)9
Skewness5.9216266
Sum205154
Variance23393.466
MonotonicityNot monotonic
2025-07-08T11:18:46.195488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 662
 
19.0%
2 279
 
8.0%
3 182
 
5.2%
4 160
 
4.6%
5 113
 
3.2%
6 97
 
2.8%
8 89
 
2.6%
7 88
 
2.5%
12 62
 
1.8%
10 61
 
1.7%
Other values (370) 1696
48.6%
ValueCountFrequency (%)
1 662
19.0%
2 279
8.0%
3 182
 
5.2%
4 160
 
4.6%
5 113
 
3.2%
6 97
 
2.8%
7 88
 
2.5%
8 89
 
2.6%
9 59
 
1.7%
10 61
 
1.7%
ValueCountFrequency (%)
2339 1
< 0.1%
2330 1
< 0.1%
1699 1
< 0.1%
1622 1
< 0.1%
1414 1
< 0.1%
1357 1
< 0.1%
1353 1
< 0.1%
1349 1
< 0.1%
1326 1
< 0.1%
1315 1
< 0.1%
Distinct20
Distinct (%)0.6%
Missing22
Missing (%)0.6%
Memory size306.5 KiB
(30451916.51, 46718747.516]
 
230
(222964579.98, 46172150151.0]
 
215
(19975532.58, 30451916.51]
 
210
(13557176.81, 19975532.58]
 
206
(46718747.516, 89439449.702]
 
205
Other values (15)
2401 

Length

Max length29
Median length28
Mean length25.107009
Min length19

Characters and Unicode

Total characters87046
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(4727330.113, 6702697.888]
2nd row(4727330.113, 6702697.888]
3rd row(3396600.0, 4727330.113]
4th row(89439449.702, 222964579.98]
5th row(2483085.385, 3396600.0]

Common Values

ValueCountFrequency (%)
(30451916.51, 46718747.516] 230
 
6.6%
(222964579.98, 46172150151.0] 215
 
6.2%
(19975532.58, 30451916.51] 210
 
6.0%
(13557176.81, 19975532.58] 206
 
5.9%
(46718747.516, 89439449.702] 205
 
5.9%
(89439449.702, 222964579.98] 200
 
5.7%
(9424898.401, 13557176.81] 200
 
5.7%
(4727330.113, 6702697.888] 178
 
5.1%
(-0.001, 33011.111] 175
 
5.0%
(6702697.888, 9424898.401] 167
 
4.8%
Other values (10) 1481
42.4%

Length

2025-07-08T11:18:46.279575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30451916.51 440
 
6.3%
46718747.516 435
 
6.3%
19975532.58 416
 
6.0%
222964579.98 415
 
6.0%
13557176.81 406
 
5.9%
89439449.702 405
 
5.8%
9424898.401 367
 
5.3%
6702697.888 345
 
5.0%
4727330.113 344
 
5.0%
3396600.0 329
 
4.7%
Other values (11) 3032
43.7%

Most occurring characters

ValueCountFrequency (%)
1 8557
9.8%
7 8083
9.3%
9 7769
8.9%
. 6934
 
8.0%
5 6907
 
7.9%
3 6577
 
7.6%
8 6556
 
7.5%
0 6206
 
7.1%
4 5479
 
6.3%
2 5093
 
5.9%
Other values (6) 18885
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87046
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8557
9.8%
7 8083
9.3%
9 7769
8.9%
. 6934
 
8.0%
5 6907
 
7.9%
3 6577
 
7.6%
8 6556
 
7.5%
0 6206
 
7.1%
4 5479
 
6.3%
2 5093
 
5.9%
Other values (6) 18885
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87046
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8557
9.8%
7 8083
9.3%
9 7769
8.9%
. 6934
 
8.0%
5 6907
 
7.9%
3 6577
 
7.6%
8 6556
 
7.5%
0 6206
 
7.1%
4 5479
 
6.3%
2 5093
 
5.9%
Other values (6) 18885
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87046
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8557
9.8%
7 8083
9.3%
9 7769
8.9%
. 6934
 
8.0%
5 6907
 
7.9%
3 6577
 
7.6%
8 6556
 
7.5%
0 6206
 
7.1%
4 5479
 
6.3%
2 5093
 
5.9%
Other values (6) 18885
21.7%
Distinct10
Distinct (%)0.3%
Missing22
Missing (%)0.6%
Memory size260.3 KiB
(0.999, 2.0]
1789 
(2.0, 3.0]
335 
(3.0, 4.0]
219 
(8.0, 12.0]
212 
(19.0, 39.0]
180 
Other values (5)
732 

Length

Max length14
Median length12
Mean length11.461783
Min length10

Characters and Unicode

Total characters39738
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(0.999, 2.0]
2nd row(6.0, 8.0]
3rd row(4.0, 5.0]
4th row(12.0, 19.0]
5th row(3.0, 4.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 1789
51.3%
(2.0, 3.0] 335
 
9.6%
(3.0, 4.0] 219
 
6.3%
(8.0, 12.0] 212
 
6.1%
(19.0, 39.0] 180
 
5.2%
(12.0, 19.0] 171
 
4.9%
(6.0, 8.0] 145
 
4.2%
(39.0, 1214.0] 144
 
4.1%
(4.0, 5.0] 138
 
4.0%
(5.0, 6.0] 134
 
3.8%
(Missing) 22
 
0.6%

Length

2025-07-08T11:18:46.360203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:46.424822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 2124
30.6%
0.999 1789
25.8%
3.0 554
 
8.0%
12.0 383
 
5.5%
4.0 357
 
5.1%
8.0 357
 
5.1%
19.0 351
 
5.1%
39.0 324
 
4.7%
6.0 279
 
4.0%
5.0 272
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 6934
17.4%
. 6934
17.4%
9 6042
15.2%
( 3467
8.7%
, 3467
8.7%
3467
8.7%
] 3467
8.7%
2 2651
 
6.7%
1 1022
 
2.6%
3 878
 
2.2%
Other values (4) 1409
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6934
17.4%
. 6934
17.4%
9 6042
15.2%
( 3467
8.7%
, 3467
8.7%
3467
8.7%
] 3467
8.7%
2 2651
 
6.7%
1 1022
 
2.6%
3 878
 
2.2%
Other values (4) 1409
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6934
17.4%
. 6934
17.4%
9 6042
15.2%
( 3467
8.7%
, 3467
8.7%
3467
8.7%
] 3467
8.7%
2 2651
 
6.7%
1 1022
 
2.6%
3 878
 
2.2%
Other values (4) 1409
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6934
17.4%
. 6934
17.4%
9 6042
15.2%
( 3467
8.7%
, 3467
8.7%
3467
8.7%
] 3467
8.7%
2 2651
 
6.7%
1 1022
 
2.6%
3 878
 
2.2%
Other values (4) 1409
 
3.5%
Distinct15
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size261.7 KiB
(0.999, 2.0]
941 
(15.0, 21.0]
212 
(4.0, 6.0]
210 
(58.0, 97.6]
201 
(11.0, 15.0]
196 
Other values (10)
1729 

Length

Max length15
Median length12
Mean length11.798223
Min length10

Characters and Unicode

Total characters41164
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(29.0, 40.0]
2nd row(21.0, 29.0]
3rd row(40.0, 58.0]
4th row(0.999, 2.0]
5th row(58.0, 97.6]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 941
27.0%
(15.0, 21.0] 212
 
6.1%
(4.0, 6.0] 210
 
6.0%
(58.0, 97.6] 201
 
5.8%
(11.0, 15.0] 196
 
5.6%
(29.0, 40.0] 190
 
5.4%
(40.0, 58.0] 184
 
5.3%
(2.0, 3.0] 182
 
5.2%
(21.0, 29.0] 180
 
5.2%
(8.0, 11.0] 178
 
5.1%
Other values (5) 815
23.4%

Length

2025-07-08T11:18:46.521563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 1123
16.1%
0.999 941
13.5%
15.0 408
 
5.8%
21.0 392
 
5.6%
6.0 387
 
5.5%
58.0 385
 
5.5%
40.0 374
 
5.4%
11.0 374
 
5.4%
29.0 370
 
5.3%
4.0 370
 
5.3%
Other values (6) 1854
26.6%

Most occurring characters

ValueCountFrequency (%)
0 6989
17.0%
. 6978
17.0%
9 3832
9.3%
( 3489
8.5%
, 3489
8.5%
3489
8.5%
] 3489
8.5%
1 2228
 
5.4%
2 1885
 
4.6%
6 1228
 
3.0%
Other values (5) 4068
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6989
17.0%
. 6978
17.0%
9 3832
9.3%
( 3489
8.5%
, 3489
8.5%
3489
8.5%
] 3489
8.5%
1 2228
 
5.4%
2 1885
 
4.6%
6 1228
 
3.0%
Other values (5) 4068
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6989
17.0%
. 6978
17.0%
9 3832
9.3%
( 3489
8.5%
, 3489
8.5%
3489
8.5%
] 3489
8.5%
1 2228
 
5.4%
2 1885
 
4.6%
6 1228
 
3.0%
Other values (5) 4068
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6989
17.0%
. 6978
17.0%
9 3832
9.3%
( 3489
8.5%
, 3489
8.5%
3489
8.5%
] 3489
8.5%
1 2228
 
5.4%
2 1885
 
4.6%
6 1228
 
3.0%
Other values (5) 4068
9.9%

cluster_k5
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size224.9 KiB
0
3489 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3489
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3489
100.0%

Length

2025-07-08T11:18:46.578672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:18:46.627692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3489
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3489
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3489
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3489
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3489
100.0%

Interactions

2025-07-08T11:18:41.612128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:39.297390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:39.746565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.197701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.629461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.162917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.678768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:39.361003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:39.830843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.261864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.695367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.245047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.761630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:39.452435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:39.906057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.348582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.777733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.312105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.830087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:39.526466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:39.979692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.413962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.846310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.395427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.895465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:39.600325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.046076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.478810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.912648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.462137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.962101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:39.680869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.129517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:40.561113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.093838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:18:41.547799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-08T11:18:46.677770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_antecedentescant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_socioscant_suspensionesdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.2740.1390.1510.0400.0460.0650.0160.0000.0420.0670.0950.0490.1180.1370.1120.0000.1280.2500.048
TipoSocietario0.2741.0000.1660.1780.1240.0000.0540.0770.0000.0800.0950.1480.0000.0550.1310.0880.0640.1590.3550.029
anio_preinscripcion0.1390.1661.0001.0000.0250.0390.0560.0590.0660.0380.0160.0530.0330.1550.1120.0590.0291.0000.1310.023
antiguedad0.1510.1781.0001.0000.0300.0430.0570.0590.3580.0370.0230.1420.0370.1710.1210.0650.221-0.9650.1400.117
cant_Apoderado0.0400.1240.0250.0301.0000.0000.3560.3700.0000.3110.5790.2850.0000.0220.0510.0210.0070.0180.0530.000
cant_antecedentes0.0460.0000.0390.0430.0001.0000.0000.0190.0340.0000.0000.0000.9360.0830.0420.0270.0830.0360.0000.000
cant_autenticado0.0650.0540.0560.0570.3560.0001.0000.1490.0000.5850.7180.0000.0000.0270.0000.0350.0160.0540.0840.000
cant_noAutenticado0.0160.0770.0590.0590.3700.0190.1491.0000.0000.4040.5660.0000.0000.0620.0350.0330.0140.0570.0000.000
cant_procesos_adjudicado0.0000.0000.0660.3580.0000.0340.0000.0001.0000.0000.0000.0540.0380.2680.0800.0690.563-0.3720.0000.325
cant_representante0.0420.0800.0380.0370.3110.0000.5850.4040.0001.0000.7500.3820.0000.0410.0230.0330.0590.0340.0000.000
cant_sinMontoLimite0.0670.0950.0160.0230.5790.0000.7180.5660.0000.7501.0000.0000.0000.0630.0290.0350.0000.0050.0700.000
cant_socios0.0950.1480.0530.1420.2850.0000.0000.0000.0540.3820.0001.0000.0000.0000.0220.0400.073-0.1390.0860.056
cant_suspensiones0.0490.0000.0330.0370.0000.9360.0000.0000.0380.0000.0000.0001.0000.0830.0430.0360.0880.0290.0000.000
dcant_procesos_adjudicado0.1180.0550.1550.1710.0220.0830.0270.0620.2680.0410.0630.0000.0831.0000.2020.1240.0990.1430.0430.094
dmonto_total_adjudicado0.1370.1310.1120.1210.0510.0420.0000.0350.0800.0230.0290.0220.0430.2021.0000.0320.2050.1020.0930.000
dtotal_articulos_provee0.1120.0880.0590.0650.0210.0270.0350.0330.0690.0330.0350.0400.0360.1240.0321.0000.0310.0560.0540.378
monto_total_adjudicado0.0000.0640.0290.2210.0070.0830.0160.0140.5630.0590.0000.0730.0880.0990.2050.0311.000-0.2360.0000.126
periodo_preinscripcion0.1280.1591.000-0.9650.0180.0360.0540.057-0.3720.0340.005-0.1390.0290.1430.1020.056-0.2361.0000.122-0.133
provincia0.2500.3550.1310.1400.0530.0000.0840.0000.0000.0000.0700.0860.0000.0430.0930.0540.0000.1221.0000.096
total_articulos_provee0.0480.0290.0230.1170.0000.0000.0000.0000.3250.0000.0000.0560.0000.0940.0000.3780.126-0.1330.0961.000

Missing values

2025-07-08T11:18:42.095496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-08T11:18:42.274428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-08T11:18:42.409742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k5
433712286089COMPAÑÍA DE HIGIENE26/10/2016InscriptoSociedad Responsabilidad Limitada20161020161.05.434774e+065.0Buenos Aires2.00.00.00.00.01.01.00.01.00.038.0(4727330.113, 6702697.888](0.999, 2.0](29.0, 40.0]0
530583184305MATAFUEGOS ORLANDO S.R.L.02/11/2016Desactualizado Por Documentos VencidosSociedad Responsabilidad Limitada20161120167.05.655964e+065.0Ciudad Autónoma de Buenos Aires3.00.00.00.00.01.01.00.01.00.022.0(4727330.113, 6702697.888](6.0, 8.0](21.0, 29.0]0
1030644877805LA BLUSERI S.A.12/09/2016InscriptoSociedad Anónima20160920165.04.224898e+065.0Ciudad Autónoma de Buenos Aires3.00.00.00.00.01.01.00.01.00.051.0(3396600.0, 4727330.113](4.0, 5.0](40.0, 58.0]0
1430710210000TECNARAN SRL06/09/2016InscriptoSociedad Responsabilidad Limitada201609201619.01.289448e+085.0Ciudad Autónoma de Buenos Aires1.00.00.00.01.01.02.00.02.00.01.0(89439449.702, 222964579.98](12.0, 19.0](0.999, 2.0]0
1733561600959Rognoni y CIA SA21/09/2016InscriptoSociedad Anónima20160920164.02.651773e+065.0Buenos Aires2.00.00.00.00.02.02.00.02.00.071.0(2483085.385, 3396600.0](3.0, 4.0](58.0, 97.6]0
1830714005789SIMA POWER SYSTEMS SRL16/08/2016Desactualizado Por ClaseSociedad Responsabilidad Limitada20160820161.09.405250e+055.0Ciudad Autónoma de Buenos Aires1.00.00.00.00.01.01.00.01.00.08.0(890758.9, 1302657.558](0.999, 2.0](6.0, 8.0]0
1930694465591ADSUR S.A..22/09/2016InscriptoSociedad Anónima20160920166.04.605003e+075.0Ciudad Autónoma de Buenos Aires2.00.00.00.01.01.01.01.02.00.01.0(30451916.51, 46718747.516](5.0, 6.0](0.999, 2.0]0
2630707870571BASESIDE S.R.L.19/08/2016InscriptoSociedad Responsabilidad Limitada201608201620.08.117709e+065.0Ciudad Autónoma de Buenos Aires2.00.00.00.01.01.01.01.02.00.06.0(6702697.888, 9424898.401](19.0, 39.0](4.0, 6.0]0
2730714749206Ebox S.A.29/09/2016InscriptoSociedad Anónima201609201622.01.224767e+085.0Ciudad Autónoma de Buenos Aires2.00.00.00.00.01.01.00.01.00.0124.0(89439449.702, 222964579.98](19.0, 39.0](97.6, 161.0]0
2830708995157Netlabs SRL24/08/2016Desactualizado Por Documentos VencidosSociedad Responsabilidad Limitada20160820163.03.583917e+065.0Ciudad Autónoma de Buenos Aires2.00.00.00.01.01.01.01.02.00.0237.0(3396600.0, 4727330.113](2.0, 3.0](161.0, 345.0]0
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k5
1003530711231214Encendido Bari SRL19/04/2018InscriptoSociedad Responsabilidad Limitada20180420181.01.179683e+063.0Ciudad Autónoma de Buenos Aires2.00.00.00.00.01.01.00.01.00.069.0(890758.9, 1302657.558](0.999, 2.0](58.0, 97.6]0
1004330713790407PEDRO GOITIA S.R.L.20/03/2017InscriptoSociedad Responsabilidad Limitada20170320171.01.997880e+074.0Ciudad Autónoma de Buenos Aires3.00.00.00.01.01.02.00.02.00.09.0(19975532.58, 30451916.51](0.999, 2.0](8.0, 11.0]0
1004430708304472DROGUERIA GENESIS S.A23/02/2017InscriptoSociedad Anónima20170220172.01.026534e+074.0Ciudad Autónoma de Buenos Aires2.00.00.00.00.02.02.00.02.00.0832.0(9424898.401, 13557176.81](0.999, 2.0](345.0, 6993.0]0
1004830714288551AGUAS ROMANO SRL01/12/2016InscriptoSociedad Responsabilidad Limitada20161220161.04.215306e+055.0Entre Rios2.00.00.00.00.01.01.00.01.00.01.0(377939.298, 599760.0](0.999, 2.0](0.999, 2.0]0
1005130707885595Dirsin Corporation S.A.23/02/2018Desactualizado Por Mantencion FormularioSociedad Anónima20180220181.03.166163e+073.0Ciudad Autónoma de Buenos Aires1.00.00.00.01.01.02.00.02.00.01.0(30451916.51, 46718747.516](0.999, 2.0](0.999, 2.0]0
1006133711043069VIA CARGO S.A.21/06/2019InscriptoSociedad Anónima20190620191.02.961003e+052.0Chubut1.00.00.00.00.01.01.00.01.00.03.0(224078.198, 377939.298](0.999, 2.0](2.0, 3.0]0
1006430683655143SERVEC S.A.29/11/2021InscriptoSociedad Anónima20211120211.05.169498e+060.0Ciudad Autónoma de Buenos Aires2.00.00.00.00.01.01.00.01.00.03.0(4727330.113, 6702697.888](0.999, 2.0](2.0, 3.0]0
1006630716032503BIOPAZ S.A.11/06/2021InscriptoSociedad Anónima20210620211.07.886492e+040.0Corrientes2.00.00.00.00.01.01.00.01.00.03.0(33011.111, 104767.373](0.999, 2.0](2.0, 3.0]0
1007030710308051ZENSEI SRL30/05/2017InscriptoSociedad Anónima20170520171.05.901429e+074.0Ciudad Autónoma de Buenos Aires1.00.00.00.00.01.01.00.01.00.01.0(46718747.516, 89439449.702](0.999, 2.0](0.999, 2.0]0
1007230518773743Hotel Astor Sociedad Anonima Comercial25/07/2022InscriptoSociedad Anónima20220720221.01.757476e+070.0Ciudad Autónoma de Buenos Aires2.00.00.00.01.01.01.01.02.00.06.0(13557176.81, 19975532.58](0.999, 2.0](4.0, 6.0]0